Data Visualization

CORH 203

Mathew Vis-Dunbar

Ocotber 2023

Themes

first … what is data visualization?

Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.

Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.

This involves abstraction – the data and patterns may be represented as shapes (form), categorized with colours, and connected via position.

Preattentive Attributes

Preattentive Attributes

Even though an object as a whole might take some conscious effort to identify, the basic visual attributes that combine to make up that object are perceived without any conscious effort.

Stephen Few (2004). Tapping the Power of Visual Perception.

Preattentive Attributes

The things we process before we’re truly cognizant of the information.

Preattentive Attributes

The things we process before we’re truly cognizant of the information.

Form.

Preattentive Attributes

The things we process before we’re truly cognizant of the information.

Form. Colour.

Preattentive Attributes

The things we process before we’re truly cognizant of the information.

Form. Colour. Position.

Preattentive Attributes

The things we process before we’re truly cognizant of the information.

Form. Colour. Position.

We can do a little test…

second … data types

Preattentive attributes + Data types

third … why visualize data?

Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication.

Stephen Few. Data Visualization for Human Perception.

Visualization in Sense Making

Data are generally summarized using test statistics; a single number to describe an entire data set.



Averages describing central tendencies

Standard deviations describing the average spread of the data

Correlation coefficients describing the direction of a relationship

Dinosaurus Dozen

dataset mean_x mean_y sd_x sd_y correlation
away 54.26610 47.83472 16.76983 26.93974 -0.0641284
bullseye 54.26873 47.83082 16.76924 26.93573 -0.0685864
circle 54.26732 47.83772 16.76001 26.93004 -0.0683434
dino 54.26327 47.83225 16.76514 26.93540 -0.0644719
dots 54.26030 47.83983 16.76774 26.93019 -0.0603414
h_lines 54.26144 47.83025 16.76590 26.93988 -0.0617148
high_lines 54.26881 47.83545 16.76670 26.94000 -0.0685042
slant_down 54.26785 47.83590 16.76676 26.93610 -0.0689797
slant_up 54.26588 47.83150 16.76885 26.93861 -0.0686092
star 54.26734 47.83955 16.76896 26.93027 -0.0629611
v_lines 54.26993 47.83699 16.76996 26.93768 -0.0694456
wide_lines 54.26692 47.83160 16.77000 26.93790 -0.0665752
x_shape 54.26015 47.83972 16.76996 26.93000 -0.0655833

Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17. https://doi.org/10.2307/2682899 Matejka, J., & Fitzmaurice, G. (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 1290–1294. https://doi.org/10.1145/3025453.3025912

Visualization for Communication

Describing the data & Telling a story

Counts, Distributions & Comparing Variables

Abstracting Further from the Data

Counts

Distributions | Bar charts & Density Plots

Distributions | Bar Charts Cont’d

Distributions | Time Series

Distributions | Time Series Cont’d

Comparing Variables | Scatter Plots

Comparing Variables | Scatter Plots

Comparing Variables | Scatter Plots

Comparing Variables | Scatter Plots

Comparing Variables | Scatter Plots

Box & Whisker

Box & Whisker Cont’d

Box & Whisker Cont’d

Box & Whisker Cont’d

Know Your Audience

A Data Table May be Enough

Or a Pie Chart

Colour Should be Meaningful

Sequential

Diverging

Qualitative

Colour Blindness